--- title: "Pydantic AI" url: "/docs/proxy/lens/integrations/pydantic-ai" canonical_url: "https://docs.litellm.ai/docs/proxy/lens/integrations/pydantic-ai" type: "docs" last_updated: "2026-10-15" summary: "Run Pydantic AI examples and send their agent traces to LiteLLM Lens." related: - "/docs/proxy/lens/integrations/crewai" - "/docs/proxy/lens/integrations/llamaindex" --- # Pydantic AI > Index of all LiteLLM docs: https://docs.litellm.ai/llms.txt # Pydantic AI For optional help from your coding agent, use [Set it up for me](https://github.com/BerriAI/lens/blob/main/docs/setup-with-agent.md#connect-an-agent-to-lens-already-running). It preserves your model connection and verifies a real trace after setup Send Pydantic AI traces to [LiteLLM Lens](/docs/proxy/lens) using the runnable examples in this repository. ## Prerequisites You need [Lens installed alongside LiteLLM](/docs/proxy/lens/deployment#configure-an-existing-proxy), a key with model access, and a configured model alias. The swarm example needs a model that supports tool calls. In **Lens > Traces > Set up tracing**, click **Generate tracing key** and copy the **Traces endpoint** under **Connection details**. Ask your administrator for these if you cannot create a tracing key. Install [uv](https://docs.astral.sh/uv/getting-started/installation/). It uses the checked-in Python version and resolves each example’s dependencies from its uv workspace. ## Configuration For a fresh checkout: ```bash git clone https://github.com/BerriAI/litellm-lens-example.git cd litellm-lens-example/pydantic-ai cp .env.example .env ``` If you already cloned the repository, run the remaining commands from `pydantic-ai/`. Copy [.env.example](https://github.com/BerriAI/litellm-lens-example/blob/a2294277609202247b71c08d7b491f5212809988/pydantic-ai/.env.example) to `.env` if it does not exist, then set: | Variable | Value | | --------------------- | ------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | | `LITELLM_GATEWAY_URL` | Your gateway’s base URL without a trailing slash or `/v1`, for example `http://localhost:4002` | | `LITELLM_API_KEY` | Your LiteLLM model key | | `LENS_URL` | Copy **Traces endpoint** from Lens tracing setup and remove the final `/v1/traces`. Keep `/lens-ingest` if present. For example, `http://localhost:4318` or `https://gateway.example/lens-ingest` | | `LENS_TRACING_KEY` | The dedicated tracing key from Lens tracing setup | | `LITELLM_MODEL` | A model alias configured on your gateway | The checked-in values target a local development gateway. Replace them for your deployment. Keep the exporter settings from `.env.example`; the examples configure their trace exporters in code. They send traces to `LENS_URL/v1/traces` with the tracing key as a bearer token. Leave `MOCK_LITELLM_GATEWAY_URL` unset unless you intend to send an additional trace copy to the local [recorder](https://github.com/BerriAI/litellm-lens-example/blob/a2294277609202247b71c08d7b491f5212809988/recorder/AGENTS.md). ## Run an example ### Simple agent A `research_agent` answers one question. ```bash uv run --env-file .env --package lens-pydantic-ai-simple simple/main.py ``` See [simple/main.py](https://github.com/BerriAI/litellm-lens-example/blob/a2294277609202247b71c08d7b491f5212809988/pydantic-ai/simple/main.py) for the implementation. ### Agent swarm A coordinator delegates to `search_agent` and `writer_agent` through tools. ```bash uv run --env-file .env --package lens-pydantic-ai-swarm swarm/main.py ``` See [swarm/main.py](https://github.com/BerriAI/litellm-lens-example/blob/a2294277609202247b71c08d7b491f5212809988/pydantic-ai/swarm/main.py) for the implementation. ### Streaming Set `LITELLM_STREAM=1` to enable streaming in either example: ```bash LITELLM_STREAM=1 uv run --env-file .env --package lens-pydantic-ai-simple simple/main.py ``` ## Validate attempts `validate_attempts.py` runs the simple agent through a fault-injecting transport wrapped by `gateway_http_client(base_url, transport=...)`: ```bash uv run --env-file .env --package lens-pydantic-ai-simple validate_attempts.py retry uv run --env-file .env --package lens-pydantic-ai-simple validate_attempts.py response-loss ``` The retry scenario replaces the first real billed response with a client-side HTTP 503 so the OpenAI client (`max_retries=1`) retries once: two `gateway.request` spans, two spend rows. The response-loss scenario consumes the real billed response and fails the client stream with no retries: one attempt, one spend row, a failed run. Neither scenario changes gateway or provider behavior. ## Verify the trace After the example prints its answer, open **Lens > Traces** on your gateway and select the new run. Look for the run associated with `research_agent`. Inspect the input, output, and model spans. For the swarm, inspect the specialist activity described above; its exact span layout depends on the framework. ## How tracing works Pydantic AI emits agent, tool, and model spans through Agent.instrument\_all(). The shared gateway transport records request attempts and gateway call IDs for matching model calls to spend. See the [shared gateway transport](https://github.com/BerriAI/litellm-lens-example/blob/a2294277609202247b71c08d7b491f5212809988/shared/README.md) for request-attempt and spend-correlation details. ## Troubleshooting If model calls fail, check the gateway URL, key, and model alias. If an answer appears but the trace is missing, check the terminal for exporter errors and confirm the Lens ingestion service is reachable with your tracing key. A model call succeeding does not confirm that its trace export succeeded. ## Related pages - [CrewAI](https://docs.litellm.ai/docs/proxy/lens/integrations/crewai.md) - [LlamaIndex](https://docs.litellm.ai/docs/proxy/lens/integrations/llamaindex.md)